Coming to grips with biological
complexity at the NIH
Eric Jakobsson
Director, NIGMS Center for Bioinformatics and Computational
Biology
Chair, NIH Biomedical Information Science and Technology
Initiative
For the Networks and Complex Systems Seminar Series
Indiana University
March 28, 2005
What are the elements of biological
complexity?
• At the molecular level, combinatorial
explosion—effectively an infinite number
of possible gene sequences and protein
structures. (Albeit a constrained infinite
number.)
• At the reaction network level, the fact that
single gene products are implicated in
multiple reaction networks.
More generally (from Tong et al, Global mapping of
the yeast genetic interaction network, Science, 2-604…)
What is forcing us to come to grips
theoretically with biological
complexity?
• On the technology-push side—capabilities for highthroughput data gathering that have made us aware that
biological networks have many more components than we
previously surmised.
• On the biology pull side---the realization that to the extent
that we don’t characterize biological systems quantitatively
in their full complexity, the scope and accuracy of our
understanding of those systems will be compromised. (In
classical experimental terms, the uncontrolled variables in
the system will undermine our confidence in the conclusions
we draw from the experiment or the observation.
How has coming to grips with
complexity made computation become
critically important to advances in
biology?
• One can’t organize the high-throughput
data into structures that are useful for
generating knowledge without computers.
• One can’t do the analysis, modeling, and
visualization without computers.
Top Ten Advances
In Biomedical Computing
In The Last Decade
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1. Sequence alignment tools.
2. Enabling Systems Biomedicine.
3. Identification of genes for disease susceptibility.
4. Computational model of HIV infection---1996
5. Tomography
6. Computer-aided Prosthetic Design.
7. Modeling electrical behavior of neurons and other electrically
excitable tissue.
• 8. Dissemination of bioinformatics tools on the Web -- 1993
• 9. Telemedicine.
• 10. Establishment of computer networks for surveillance of disease.
“Systems Biology” is an approach to
understanding biological complexity. What
are the elements of systems biology?
• Make a parts list (based on experiment)
• Make a network diagram to see how the parts are
interconnected. (Inference from experiment and
theory)
• If you are able to do so, make a
mathematical/computational model of the system
that will test the completeness and correctness of
your understanding and provide a guide for future
experiments.
Differences between systems biology and
traditional cell and molecular biology
• Experimental techniques in systems biology are high
throughput.
• Intensive computation is involved from the start in systems
biology, in order to organize the data into usable
computable databases.
• Exploration in traditional physiology proceeds by
successive cycles of hypothesis creation and hypothesis
testing; data stores accumulate during these cycles.
• Systems biology initially gathers data without prior
hypothesis creation; hypothesis creation and testing comes
during post-experiment data analysis and modeling.
A simple computer experiment using
prior data stores that were prehypothetically assembled.
• Question: Is the plot of “Clan of the Cave Bear”, involving
interbreeding between Neanderthal and anatomically
modern humans, biologically plausible?
• Relevant data in Genbank: Mitochondrial DNA sequences
from Neanderthals, modern humans, and other mammals.
• Hypothesis: Degree of relatedness of DNA may be a
marker for possibility of interbreeding.
• Computer experiment: Compare comparable DNA
sequences from Neanderthals, modern humans other
mammals including species that interbreed and those that
do not.
First, search Genbank for
Neanderthal DNA sequences…
Then, use one of the Neanderthal
sequences as a BLAST probe to find
its most closely related human
sequence.
And also use the Neanderthal
sequence as blast probe to find
corresponding sequences in other
mammals.
Next, align the sequences…
And draw a phylogenetic tree…
Elements of systems biology in the
illustration
• Made a parts list from pre-existing
“comprehensive” data
• Used a mathematical model to interpret
functional relationships among the parts
from the data
• Drew a tentative conclusion but need more
data
What is the range of viewpoints on
systems biology?
Top ten challenges for computational biology in the
next decade
1.
2.
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4.
5.
6.
7.
8.
9.
10.
In silico screening of drug compounds
Predicting function from structure of complex molecules at an
engineering level of precision
Prediction of protein structure
Accurate, efficient, and comprehensive dynamic models of the
spread of infectious disease
Intelligent systems for mining biomedical literature
Complete annotation of the genomes of selected model
organisms
Improved computerization of the health-care delivery system
Making systems biology a reality by integrating appropriate
computational tools
Tuning biomedical computing software to computer hardware
Promoting the use of computational biology tools in education
Some important problems with
biomedical computing tools are:
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They are difficult to use.
They are fragile.
They lack interoperability of different components
They suffer limitations on dissemination
They often work in one program/one function
mode as opposed to being part of an integrated
computational environment.
• There are not sufficient personnel to meet the
needs for creating better biological computing
tools and user environments.
II. We want you if…….
• You have ideas that can contribute to
realizing the NIH vision.
• You have the skills and motivation to
implement those ideas.
• The right vehicle for realizing your ideas is
EITHER a large project or a small project.
Looking at the NIH for support for
computational projects I: General
Issues
• First Principle: NIH is a mission-driven agency. We
support basic science (lots of it) and technology and
infrastructure development (on an increasing trend line),
but it all must be justifiable by a payoff down the line in
improving the health of the American people.
• Corollary Principle: We understand that the payoff may
not be immediate, so we support work where the payoff is
a decade or more in the future. It is better to present a
justification for a reasonable but long-term payoff than an
unrealistic short-term payoff.
Looking at the NIH for support for Computational
Projects II: Perspectives on the Role of Computation
in Biomedical Research and Health Care Delivery
• We see that non-trivial computation is critical to every
aspect of our mission, from the most basic research to the
efficient and effective delivery of health care in all venues.
• We see the corollary: Inefficiencies, gaps, and flaws in
computation are limiting the pace and scope of all aspects
of our mission.
• We have only gotten the message recently, so we are a
work-in-progress with respect to implementing our
understandings about computation in programs and
practices.
• We need computer scientists, computational scientists, and
information technologists to be partners with NIH in
getting it right.
Looking at the NIH for support for computational
projects III: Finding out what NIH actually funds
• CRISP data base (Google “NIH CRISP” provides
keyword-searchable database of all NIH-funded
projects from 1972-2004
• Comprehensive access to publications by NIH
grantees provided by author-searchable Pubmed
literature database (Google “pubmed”)
Looking at the NIH for support for computational projects
IV: Building on your knowledge of what we now do to what
we might support you for doing
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First-stop (but not “one stop”) information source is the BISTI home page
(Google “NIH BISTI”), button under “Funding”
If you don’t find a funding announcement that fits your ideas/capabilities, but
you feel you have something to contribute, don’t hesitate to send an unsolicited
application. (Receipt dates February 1, June 1, and October 1 each year for new
applications). Success rates for unsolicited applications are often as good as, in
some cases better than, success rates for proposals submitted in response to
specific funding announcements.
Consult with an NIH Program Director at the concept development stage. This
is easy if you are responding to a funding announcement—the right contact
information is in the funding announcement. For an unsolicited application, you
may need to browse through Web sites for many of the semi-autonomous 27
Institutes and Centers that comprise the NIH, as well as the NIH Roadmap site,
that contains information on NIH-wide initiatives. But---NIH is a strongly
interconnected community, so if you start calling program staff and the first
person you call is not the right person, you will get good direction to the right
person fairly quickly.
Looking at the NIH for support for computational projects IV:
Building on your knowledge of what we now do to what we
might support you for doing (continued)
• Research study sections as well as programs (Google NIH CSR),
button under “Study Section Information.”
• On study section targeting, consult with Program Director and/or
Scientific Review Administrator (Understand that program and review
functions at NIH collaborate with each other but are independently
accountable. This is different from NSF, where the same individuals
are responsible for both creating program and overseeing review. With
respect to NIH review issues, the AUTHORITATIVE information
comes from the review side)
• FOLLOW THE RULES AND GUIDELINES! (Google “NIH 398” in
addition to particular funding announcements.) That gives program and
review staff more time to deal with your scientifically substantive
concerns, because they won’t have to work around emergent
procedural issues.
Looking at the NIH for support for computational projects IV:
Building on your knowledge of what we now do to what we
might support you for doing (final)
• Develop an NIH “grant journal club” (or
comparable structure) at your institution
where colleagues read and critique each
other’s NIH grant applications and progress
reports in preparation.
THE BEGINNING
Thank you for your attention and
your commitment to building the
future